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RANDOMIZED ITERATIVE FEEDBACK TUNING
László Gerencsér* Zsuzsanna Vágó**
Håkan Hjalmarsson***
* Computer and Automation Institute, Hungarian Academy of
Sciences, 13-17 Kende u., Budapest 1111, Hungary
gerencser@sztaki.hu
** Computer and Automation Institute, Hungarian Academy of
Sciences, 13-17 Kende u., Budapest 1111, Hungary
and Pázmány P. Catholic University, Budapest,
vago@oplab.sztaki.hu
*** Dept. of Signals, Sensors and Systems
The Royal Institute of Technology, S-100 44 Stockholm, Sweden
hakan.hjalmarsson@s3.kth.se
In this contribution we present a controller tuning method which combines some of the advantages of Iterative Feedback Tuning (IFT) with some of the advantages of simultaneous perturbation stochastic approximation (SPSA). In particular this scheme can be shown to converge with geometric rate when a pure gradient search is used and the system is noise free. The number of experiments required to obtain an unbiased estimate of the gradient can be reduced significantly for multi-input multi-output systems. In particular we study the problem where the reference signal is periodic and when the noise is negligible.
Keywords: adaptive algorithms, iterative methods
Session slot T-Th-A03: Iterative Control Design/Area code 3b : Adaptive Control and Tuning
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